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View details ↗DISCOVER & COMPARE
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Nova 2.0 Omni (Low)
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View details ↗Nova 2.0 Omni (Medium)
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View details ↗Nova 2 Lite
amazonNova 2 Lite is a fast, cost-effective reasoning model for everyday workloads that can process text, images, and videos to generate text. Nova 2 Lite demonstrates standout capabilities in processing...
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$0.3 / M input tokensOpenRouter · amazon/nova-2-lite-v1
View details ↗Nova Lite
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View details ↗Nova Lite 1.0
amazonAmazon Nova Lite 1.0 is a very low-cost multimodal model from Amazon that focused on fast processing of image, video, and text inputs to generate text output. Amazon Nova Lite...
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$0.06 / M input tokensOpenRouter · amazon/nova-lite-v1
View details ↗Nova Micro
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View details ↗Nova Micro 1.0
amazonAmazon Nova Micro 1.0 is a text-only model that delivers the lowest latency responses in the Amazon Nova family of models at a very low cost. With a context length...
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$0.035 / M input tokensOpenRouter · amazon/nova-micro-v1
View details ↗Nova Premier
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View details ↗Nova Premier 1.0
amazonAmazon Nova Premier is the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models.
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$2.5 / M input tokensOpenRouter · amazon/nova-premier-v1
View details ↗Nova Pro
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View details ↗Nova Pro 1.0
amazonAmazon Nova Pro 1.0 is a capable multimodal model from Amazon focused on providing a combination of accuracy, speed, and cost for a wide range of tasks. As of December...
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$0.8 / M input tokensOpenRouter · amazon/nova-pro-v1
View details ↗Titan-text-embeddings-v2
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View details ↗**Purpose:** This module takes a search query [1] and a passage [2] and calculates if the passage matches the query. It can be used as an improvement for Elasticsearch Results and boosts the relevancy by up to 100%. **Architecture:** On top of BERT there is a Densly Connected NN which takes the 768 Dimensional [CLS] Token as input and provides the output (Arxiv). **Output:** Just a single value between between -10 and 10. Better matching query,passage pairs tend to have a higher a score.
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View details ↗Qwen-HTML
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View details ↗vl-2025
ambitious479基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗AMD-Llama-135m is a language model trained on AMD Instinct MI250 accelerators. Based on LLama2 model architecture, this model can be smoothly loaded as LlamaForCausalLM with huggingface transformers. Furthermore, we use the same tokenizer as LLama2, enabling it to be a draft model of speculative decoding for LLama2 and CodeLlama. AMD-Llama-135m and AMD-Llama-135m-code can be loaded and used via huggingface transformers, here is a simple example. We use SlimPajama and project gutenberg dataset to pretrain our 135m model, around 670B training tokens in total. SlimPajama is a deduplicated version of RedPajama and sources from Commoncrawl, C4, GitHub, Books, ArXiv, Wikpedia and StackExchange. We droped the Books data from SlimPajama due to license issues and used project gutenberg dataset instead.
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